\section{Conclusion}
\label{section:conclusion}
We propose HashFlow for efficient collection of flow records, 
which is useful for a wide range of measurement and analysis applications. 
The collision resolution and record promotion strategy is of central importance 
to HashFlow's accuracy and efficiency. We analyze the performance bound 
of HashFlow based on a probabilistic model, and implement it in a software switch as well as a hardware switch. 
The evaluation results based on real traces from different networks show that, 
HashFlow consistently achieves a clearly better performance in nearly all cases. 
In the future, we plan to study how to make it adaptive to more accurate measurement of mice flows and network wide measurement.
